The article forecasts credit ratings of EU banks using alternative models based on financial data. By analyzing 112 EU banking institutions, the study found that a Support Vector Machines model with a nonlinear RBF kernel accurately predicted 91.07% of banks' ratings using only 8 variables. This suggests that publicly available financial information can closely approximate official ratings by Credit Rating Agencies.